Enabling Gradient-Structured Solid Electrolyte Interphase by a Hydrated Eutectic Electrolyte for High-Performance Zn Metal Batteries.

Aqueous Zn metal batteries are attracting tremendous interest as promising energy storage systems due to their intrinsic safety and cost-effectiveness. Nevertheless, the reversibility of Zn metal anodes (ZMAs) is hindered by water-induced parasitic reactions and dendrite growth. Herein, a novel hydrated eutectic electrolyte (HEE) consisting of Zn(BF4)2·xH2O and sulfolane (SL) is developed to prevent the side reactions and achieve the outstanding cyclability of ZMAs. The strong coordination between Zn2+ and SL triggers the eutectic feature, enabling the low-temperature availability of HEEs. The restriction of BF4 - hydrolysis in the eutectic system can realize favorable compatibility between Zn(BF4)2-based electrolyte and ZMAs. Besides, the newly-established solvation structure with the participation of SL, H2O, and BF4 -, can induce in situ formation of desirable SEI with gradient structure consisting of B,O-rich species, ZnS, and ZnF2, to offer satisfactory protection toward ZMAs. Consequently, the HEE allows the Zn||Zn symmetric cell to cycle over 1650 h at 2 mA cm-2 and 1 mA h cm-2. Moreover, the Zn||NH4V4O10 full batteries can deliver a prolonged lifespan for 1000 cycles with a high capacity retention of 83.4%. This work represents a feasible approach toward the elaborate design of advanced electrolyte systems for next-generation batteries.

CT-based intratumoral and peritumoral deep transfer learning features prediction of lymph node metastasis in non-small cell lung cancer.

BACKGROUND: The main metastatic route for lung cancer is lymph node metastasis, and studies have shown that non-small cell lung cancer (NSCLC) has a high risk of lymph node infiltration. OBJECTIVE: This study aimed to compare the performance of handcrafted radiomics (HR) features and deep transfer learning (DTL) features in Computed Tomography (CT) of intratumoral and peritumoral regions in predicting the metastatic status of NSCLC lymph nodes in different machine learning classifier models. METHODS: We retrospectively collected data of 199 patients with pathologically confirmed NSCLC. All patients were divided into training (n = 159) and validation (n = 40) cohorts, respectively. The best HR and DTL features in the intratumoral and peritumoral regions were extracted and selected, respectively. Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Light Gradient Boosting Machine (Light GBM), Multilayer Perceptron (MLP), and Logistic Regression (LR) models were constructed, and the performance of the models was evaluated. RESULTS: Among the five models in the training and validation cohorts, the LR classifier model performed best in terms of HR and DTL features. The AUCs of the training cohort were 0.841 (95% CI: 0.776-0.907) and 0.955 (95% CI: 0.926-0.983), and the AUCs of the validation cohort were 0.812 (95% CI: 0.677-0.948) and 0.893 (95% CI: 0.795-0.991), respectively. The DTL signature was superior to the handcrafted radiomics signature. CONCLUSIONS: Compared with the radiomics signature, the DTL signature constructed based on intratumoral and peritumoral areas in CT can better predict NSCLC lymph node metastasis.

Improving the spectral efficiency of visible light communications by a super-Nyquist multi-band CAP modulation.

In this Letter, we propose a flexible bandwidth compression scheme for visible light communication (VLC) systems employing multi-band carrierless amplitude and phase (CAP) modulation. The scheme combines a narrow filtering for every subband at the transmitter and an N-symbol look-up-table (LUT) based maximum likelihood sequence estimation (MLSE) at the receiver. The N-symbol LUT is generated by recording pattern-dependent distortions induced by inter-symbol-interference (ISI), inter-band-interference (IBI), and the other channel effects upon the transmitted signal. The idea is experimentally demonstrated on a 1 m free space optical transmission platform. The results show that the proposed scheme can improve the subband overlap tolerance up to 42% in subband overlapping scenarios, that is, 3 bit/s/Hz, which is the highest spectral efficiency (SE) among the experimented schemes.

Effect of artemisinin on neuropathic pain mediated by P2X4 receptor in dorsal root ganglia.

Neuropathic pain is a type of chronic pain caused by nervous system damage and dysfunction. The pathogenesis of chronic pain is complicated, and there are no effective therapies for neuropathic pain. Studies show that the P2X4 receptor expressed in the satellite glial cells (SGCs) of dorsal root ganglia (DRG) is related to neuropathic pain. Artemisinin is a monomeric component extracted from traditional Chinese medicine and has a variety of important pharmacological effects and potential applications. This study observed the effect of artemisinin on neuropathic pain and delineated its possible mechanism. The chronic constriction injury (CCI) rat model was used in this study. The results demonstrated that artemisinin relieved pain behaviors in the CCI rats, inhibited the expression of P2X4 receptor in the DRG, and decreased the ATP-activated currents in HEK293 cells transfected with P2X4 plasmid. Dual-labeling immunofluorescence showed that the coexpression of P2X4 receptor and glial fibrillary acidic protein (GFAP) in the DRG of CCI rats was increased compared to control rats. After CCI rats were treated with artemisinin, the coexpression of P2X4 receptor and GFAP in the DRG was significantly decreased compared to the CCI group. This finding suggested that artemisinin could inhibit the nociceptive transmission mediated by P2X4 receptor in the DRG SGCs and thus relieve pain behaviors in the CCI rats.